What this category of data looks like
Data sources for carbon steel investment research data include publicly available statistical data from the China Iron and Steel Industry Association, official monthly operation briefings from steel mills, carbon steel futures market data on the Shanghai Futures Exchange, and brokerage industry research reports. Update frequencies fall into three categories: spot price data is updated daily, production capacity and inventory data is updated monthly, and industry supply and demand analysis reports are updated quarterly.
Document structures are divided into three types: structured quotation sheets (fields include carbon steel product name, specification model, production origin, tax-included ex-factory price, price change range), semi-structured research reports (including core indicator tables, supply and demand balance data), and unstructured industry analysis documents. Field units uniformly use yuan/ton, ten thousand tons, and percentage.
What constraints do these characteristics impose on the "model access and configuration" link
Since carbon steel data includes a large number of structured quotation sheets, data sources with multiple update cycles, and some research reports contain image content such as production report screenshots, multiple constraints apply to the model access and configuration workflow.
Adapt to the semantic extraction logic of structured text, and support incremental data synchronization across different update frequencies. Configure model channels that support image-text recognition to extract structured data from images. Accurately match carbon steel sub-categories and fields to avoid invalid cross-category recall. Adapt parsing durations for different document lengths to avoid timeout interruptions.
How to Set Configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
embedding_model | Select m3e-large | Carbon steel data contains a large number of structured fields, and this model delivers more stable semantic encoding effects for structured text |
maxContext | 800–1200 characters | Adapts to the average text length of carbon steel research reports and quotation sheets, avoiding truncation of core data |
recall_top_k | Top 6 entries | There are many carbon steel sub-categories, so sufficient candidate documents must be recalled to cover relevant category data |
similarity_threshold | 0.75–0.85 | Filters low-match cross-category documents to avoid invalid recall |
PARSE_FILE_TIMEOUT_SECONDS | 120 seconds | Adapts to the parsing duration of longer industry research report documents, avoiding parsing timeouts |
enable_image_parse | Enabled | Adapts to image content such as production report screenshots included in research reports, extracting structured data from them |
The parameter values provided on this page are common recommended starting points for determining configurations. Actual values are affected by material form, data volume, and business rules. Specific issues require specific analysis, and it is recommended to test on your own samples before finalizing settings.
Three Common Configuration Errors
- Setting
embedding_modeltom3e-baseleads to low recall matching accuracy for structured quotation sheets. The root cause is that this model has insufficient semantic encoding capabilities for structured fields, and cannot accurately match sub-fields such as carbon steel product names and specifications. - Model channel and token configurations are lost after service restart. The root cause is that configurations are only saved in memory and not persisted to local or cloud storage; memory data is cleared when the service restarts.
- Reranking model images fail to load when deploying on arm architecture devices. The root cause is that model images adapted to the arm architecture are not selected, and compatible parameters for cross-architecture operation are not configured, resulting in a mismatch between the image and hardware architecture.
How to Confirm Successful Configuration
- Upload a carbon steel structured quotation sheet document, check whether the recall results include matching product names and specification fields, and verify that the number of recalled entries matches the configured
recall_top_kvalue. - Restart the service, access the model configuration page, check whether the previously set channels and tokens are still retained, and confirm that configuration persistence takes effect.
- Upload a carbon steel research report PDF document containing production report screenshots, check whether the parsing results extract table data from the images, and confirm that the image-text recognition configuration takes effect.
- Deploy the reranking model on an arm architecture device, check that there are no architecture incompatibility errors in the model loading logs, and confirm that the adaptation configuration is correct.
Question material comes from public community discussions. Configuration values are common starting points and should be measured against your own samples. Verified on 2026-09-14.